Part 1: PCA with penguins

#wrangle to just variables for PCA and scale
penguin_pca <- penguins %>% 
  select(body_mass_g, ends_with("_mm")) %>% 
  drop_na() %>% 
  scale() %>% 
  prcomp() 

penguin_pca$rotation #these are the loadings
##                          PC1         PC2        PC3        PC4
## body_mass_g        0.5483502 0.084362920 -0.5966001 -0.5798821
## bill_length_mm     0.4552503 0.597031143  0.6443012 -0.1455231
## bill_depth_mm     -0.4003347 0.797766572 -0.4184272  0.1679860
## flipper_length_mm  0.5760133 0.002282201 -0.2320840  0.7837987
#need observations to match those of the PCS but still contain other variables (species)

penguin_complete <- penguins %>% 
  drop_na(body_mass_g, ends_with("_mm"))

#recognize type of data and assume what type of plot to make
autoplot(penguin_pca, data = penguin_complete,
         colour = 'species',
         loadings = TRUE, 
         loadings.label = TRUE) +
  theme_minimal()
## Warning: `select_()` is deprecated as of dplyr 0.7.0.
## Please use `select()` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_warnings()` to see where this warning was generated.

Part 2: ggplot 2 customization & reading in different file types

Read in an .xlsx file & do some wrangling

fish_noaa <- read_excel(here::here("data","foss_landings.xlsx")) %>%
  clean_names() %>% 
  mutate(across(where(is.character), tolower)) %>% 
  mutate(nmfs_name = str_sub(nmfs_name, end =-4)) %>% 
  filter(confidentiality == "public")

Make a customized graph:

fish_plot <- ggplot(data = fish_noaa, aes(x = year, y = pounds))+
  geom_line(aes(color = nmfs_name), show.legend=FALSE) +
  theme_minimal()

fish_plot 
## Warning: Removed 6 row(s) containing missing values (geom_path).

ggplotly(fish_plot)
## Use gghighlight to highlight certain series

ggplot(data = fish_noaa, aes(x = year, y = pounds, group = nmfs_name))+
  geom_line(aes(color = nmfs_name)) +
  theme_minimal()+
  gghighlight(max(pounds) > 1e8)
## label_key: nmfs_name
## Warning: Removed 6 row(s) containing missing values (geom_path).

Read in from a URL, lubridate(), mutate(), make months in logical order

monroe_wt <- read_csv("https://data.bloomington.in.gov/dataset/2c81cfe3-62c2-46ed-8fcf-83c1880301d1/resource/13c8f7aa-af51-4008-80a9-56415c7c931e/download/mwtpdailyelectricitybclear.csv") %>% 
  clean_names()
## Parsed with column specification:
## cols(
##   date = col_character(),
##   kWh1 = col_double(),
##   kW1 = col_double(),
##   kWh2 = col_double(),
##   kW2 = col_double(),
##   solar_kWh = col_double(),
##   total_kWh = col_double(),
##   MG = col_double()
## )
monroe_ts <- monroe_wt %>% 
  mutate(date = mdy(date)) %>% 
  mutate(record_month = month(date)) %>% 
  mutate(month_name = month.abb[record_month]) %>% 
  mutate(month_name = fct_reorder(month_name, record_month))

ggplot(data = monroe_ts, aes(x = month_name, y = total_k_wh))+
  geom_jitter()

#month.name is full name

Part 3: Compound figures with patchwork

graph_a <- ggplot(data=penguins, aes(x=body_mass_g, y= flipper_length_mm))+
  geom_point() 

graph_b <- ggplot(data = penguins, aes(x=species, y=flipper_length_mm))+
  geom_jitter(aes(color=species), show.legend=FALSE)

graph_c <- (graph_a | graph_b)/fish_plot & theme_dark()

graph_c 
## Warning: Removed 2 rows containing missing values (geom_point).

## Warning: Removed 2 rows containing missing values (geom_point).
## Warning: Removed 6 row(s) containing missing values (geom_path).

ggsave(here::here("fig","graph_c_lk.png"), width = 5, height = 6)
## Warning: Removed 2 rows containing missing values (geom_point).
## Warning: Removed 2 rows containing missing values (geom_point).
## Warning: Removed 6 row(s) containing missing values (geom_path).